Introduction

Meta-analysis is a statistical technique for amalgamating,
summarising, and reviewing previous quantitative
research. By using meta-analysis, a wide variety of questions can be
investigated, as long as a reasonable body of primary research studies exist.
Selected parts of the reported results of primary studies are entered into
a database, and this "meta-data" is "meta-analyzed", in similar ways to
working with other data - descriptively and then inferentially to test certain
hypotheses.

Meta analysis can be used as a guide to answer the
question 'does what we are doing make a difference to X?', even if 'X' has
been measured using different instruments across a range of different
people. Meta-analysis provides a systematic overview of quantitative
research which has examined a particular question.

The appeal of meta analysis is that it in effect combines
all the research on one topic into one large study with many participants.
The danger is that in amalgamating a large set of different studies the
construct definitions can become imprecise and the results difficult to
interpret meaningfully.

Not surprisingly, as with any research technique, meta-analysis has its
advantages and disadvantages. An advantage is its objectivity, and yet like any research, ultimately its value depends on
making some qualitative-type contextualizations and understandings of the
objective data.

Tests of statistical significance can also be conducted and on the effect
sizes.

Different effect sizes are calculated for different constructs of
interest, as predetermined by the researchers based on what issues are of
interest in the research literature.

Rules of thumb and comparisons with
field-specific benchmarks can be used to interpret effect sizes.
According to an arbitrary but commonly used interpretation of effect size
by Cohen (1988), a standardised mean effect size of 0 means no change,
negative effect sizes mean a negative change, with .2 a small change, .5 a
moderate change, and .8 a large charge. Wolf (1986), on the other hand,
suggests that .25 is educationally significant and .50 is clinically
significant.

Using Effect Sizes in Primary Studies

Meta-analysis methodologies, particularly effect sizes, are
also applicable to primary research. For example, effect sizes are particularly useful in
program evaluation studies. For more information: